We are looking for a Lead Engineer (Engineering Lead) to lead our backend, data engineering, and infrastructure efforts. Youll be responsible for building scalable data pipelines, backend services, and cloud infrastructure to power our AI-driven platform. Leading the entire engineering team. Help founders foster a high-performance engineering culture with best coding practices, documentation, and DevOps workflows.
Key Responsibilities :
Tech Leadership & Strategy :
- Lead backend, data engineering, and infrastructure efforts, ensuring scalability and reliability.
- Drive microservices architecture and serverless implementations on AWS.
- Collaborate with AI engineers to integrate LLM-powered features efficiently.
Backend & Data Engineering :
Architect and develop high-performance APIs using Django & NodeJS.Build scalable ETL pipelines to collect, clean, and update university and course data.Work with RDS, DynamoDB, and MongoDB for structured and unstructured data management.Cloud & DevOps :
Implement CI / CD pipelines, containerization (Docker, Kubernetes), and AWS infrastructure (Lambda, APIGateway, ALB, Amplify etc.).
Ensure scalability, security, and cost efficiency of cloud infrastructure.Automate deployments and optimize system performance.Team Leadership & Growth :
Lead and mentor backend and DevOps engineers.Foster a high-performance engineering culture with best coding practices, documentation, and DevOps workflows.What makes you a great fit?
5- 8 years of experience in backend development, data engineering, and cloud infrastructures.Ability to work in a fast-paced startup environment and make key architectural decisions.Expertise in Python (Django) and Node.js (Nest.js) for backend development.Strong experience with AWS (Lambda, API Gateway, RDS, DynamoDB, CI / CD, Microservices, DevOps).Experience building ETL pipelines and managing large datasets for AI-powered applications.Hands-on knowledge of infrastructure as code (Terraform, AWS CDK) and containerization (Docker / Kubernetes) is preferred.Experience working with AI / ML teams, particularly in RAG and LLM fine-tuning workflows, is a plus.(ref : hirist.tech)